Phishing URL Detection Using Machine Learning Learning And Flask Frame Work

YEAR : 2022


The emergence of artificial intelligence technology has promoted the development of the Internet of Things. However, this promising cyber technology can encounter serious security problems while accessing the internet. Thus, it is very important to detect malicious websites using tools such as machine learning algorithms such as Random Forest, Support Vector Machine, Decision Tree, Extra Tree Classifier, K-Nearest Neighbor, XG Boost, Cat Boost, Multilayer Perceptron and Gradient Boost Algorithm as these algorithms can help us to identify abnormal information hidden in the mass traffic more easily. Accordingly, many feature engineering tasks must be performed from memory, as a strong machine learning model is greatly improved with good features. In this project, we propose an unsupervised learning algorithm that learns URL embedding. We also create web application to identifying threating url with Flask framework.


System Requirements

Operating System : Windows 7,8,10 (64 bit)
Software : Python
Tools : Anaconda (Jupyter notebook and anaconda prompt)


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